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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Teknologi dan Manajemen Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Inovasi Teknologi Pendidikan Register: Jurnal Ilmiah Teknologi Sistem Informasi Briliant: Jurnal Riset dan Konseptual Sinkron : Jurnal dan Penelitian Teknik Informatika Syntax Literate: Jurnal Ilmiah Indonesia JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL EDUCATION AND DEVELOPMENT MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Entrepreneur dan Entrepreneurship (JEE) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer bit-Tech Jurnal Leverage, Engagement, Empowerment of Community (LeECOM) Jurnal Teknik Informatika (JUTIF) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pendidikan dan Pengabdian Masyarakat JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Evan Tanuwijaya
Universitas Ciputra Surabaya
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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Implementasi Website Sekolah sebagai Strategi Komunikasi dan Branding SD Kristen YBPK Ngaglik Yuwono Marta Dinata; Yobel Nathaniel Filipus; Patrick Steven Kent Sugiarto; Joren Alexander Toding; Evan Tanuwijaya
Jurnal Leverage, Engagement, Empowerment of Community (LeECOM) Vol. 8 No. 1 (2026): Jurnal Leverage, Engagement, Empowerment of Community (LeECOM)
Publisher : Universitas Ciputra Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37715/leecom.v8i1.5953

Abstract

Kegiatan ini bertujuan untuk merancang, mengimplementasikan, dan melakukan transfer pengetahuan mengenai pengelolaan situs web sekolah sebagai media informasi yang mudah diakses. Metode pelaksanaan meliputi sosialisasi, penerapan teknologi, pelatihan penggunaan, dan evaluasi. Situs web yang dihasilkan memuat informasi dasar seperti profil sekolah, visi dan misi, struktur organisasi, kontak, serta informasi penting lainnya yang dapat diakses oleh publik. Berdasarkan pengujian akses, situs web dapat dimuat dalam waktu kurang dari tiga detik dan tersedia selama 24 jam sehari. Hasil evaluasi menunjukkan bahwa kehadiran situs web sangat membantu sekolah dalam menjangkau masyarakat secara lebih luas. Kepala Sekolah SD Kristen YBPK Ngaglik menyatakan bahwa situs web tersebut mempermudah penyampaian profil sekolah kepada calon wali murid dan masyarakat umum tanpa ketergantungan pada media cetak. Selain itu, situs web ini menjadi sarana presentasi digital yang digunakan dalam berbagai forum. Program ini memberikan dampak nyata terhadap peningkatan visibilitas dan citra digital SD Kristen YBPK Ngaglik di tengah masyarakat.
Application of YOLO11 and Long Short-Term Memory Architecture for Exercise Form Evaluation in Weightlifting Nicholas Dylan Lienardi; Evan Tanuwijaya
Jurnal Teknologi dan Manajemen Informatika Vol. 11 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v11i2.16112

Abstract

Exercise provides significant benefits for physical health, and weightlifting has become increasingly popular among fitness enthusiasts. However, improper lifting techniques often lead to injuries, discouraging beginners and affecting long-term training consistency. To address this issue, this study proposes a deep learning approach that automatically evaluates weightlifting form through movement classification. The proposed method integrates the YOLO11n-pose algorithm for detecting keypoints from exercise video recordings and the Long Short-Term Memory (LSTM) network for classifying movement types and determining the correctness of form execution. The model achieved a mean average precision of 88.8% using side-view recordings of single- repetition weightlifting exercises. YOLO11n-pose extracts the coordinates of body keypoints, which are converted into joint angle data and analyzed over time using LSTM to identify movement quality based on expert-validated training data. The trained model was implemented into an iOS application called KorForm, developed using FastAPI, to provide real-time feedback for users. The results demonstrate that combining YOLO11n-pose and LSTM effectively supports weightlifting form evaluation and offers a practical solution for promoting safer and more consistent exercise habits.
Rule-Based Pitch Inference in Optical Music Recognition on Polyphonic Scores using YOLOv12 Derend Marvel Hanson Prionggo; Evan Tanuwijaya
Jurnal Teknologi dan Manajemen Informatika Vol. 11 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v11i2.16291

Abstract

Optical Music Recognition (OMR) faces significant challenges when applied to polyphonic music scores, due to the high symbol density and the overlapping of notes. This study proposes a hybrid method of combining the detection of noteheads using YOLOv12 with rule-based pitch inference, which converts the spatial position of the detected noteheads into accurate pitch information. The dataset used in this study is DeepScoresV2-Dense, which is processed through annotation conversion, image normalization, and staff extraction as a reference to infer the pitch of a note. The YOLOv12 model was trained for 30 epochs using a transfer learning approach, resulting in an mAP50 value of 0.75, a precision of 0.85, and a recall of 0.58 on the validation data. The implementation of rule-based pitch inference successfully achieved a pitch accuracy of 0.87 with an F1 score of 0.87, demonstrating a balance between accuracy and completeness of prediction. This result shows that the integration of YOLOv12 and rule-based pitch inference can be an effective solution for pitch extraction in polyphonic music scores, with potential applications in music information retrieval, digital music score conversion, and an artificial intelligence-based music learning system.
Perbandingan Kinerja Model CNN untuk Klasifikasi Kematangan Pisang pada Android Low-End Owen Orlando; Evan Tanuwijaya
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3442

Abstract

The ripeness level of bananas is an important indicator that determines the quality, selling value, and suitability of distribution in the agricultural supply chain. However, manual maturity assessments are still subjective and difficult to apply consistently on a large scale. The use of Convolutional Neural Networks (CNN) offers a more accurate and objective solution, but most previous studies have only evaluated high-powered devices so they do not reflect the real performance of low-spec smartphones. This study aims to compare the efficiency of three lightweight CNN architectures: MobileNetV1, EfficientNetB0, and NASNetMobile for the classification of banana ripeness and evaluate its feasibility of being implemented on low-end Android devices. The research method included model training using the Banana Ripeness Classification dataset containing 13,478 images with three maturity classes. Augmentation-based oversampling was applied to address data imbalances, while all three models were trained on transfer learning strategies before being converted to the TensorFlow Lite format. Direct testing was conducted on the Samsung Galaxy A3 (2016) device to measure accuracy, inference time, model size, and RAM usage. The experimental results showed that MobileNetV1 provided the best performance with an accuracy of 98.14%, an inference time of 287.57 ms, and a model size of 3.23 MB, much more efficient than EfficientNetB0 and NASNetMobile. In conclusion, MobileNetV1 is the most optimal architecture for Android-based banana ripeness classification applications on low-spec devices, while making an empirical contribution to the selection of efficient CNN models for mobile implementation in the context of digital agriculture.
Co-Authors AA Sudharmawan, AA Agus Zainal Arifin Angelica Roseanne Arianindita, Achmad Rijalu Arif Mudi Priyatno Baskoro Nugroho Chastine Fatichah Christian Tanjono Cicilia Larasati Rembulan David Christian Kartamihardja Derend Marvel Hanson Prionggo Dini Adni Navastara, Dini Adni Eha Renwi Astuti Jimmy Ellya Kurniawan Joren Alexander Toding Kenneth Sutjiawan Kristanto, Samuel Miracle Kristanto, Timothy Kuncoro Dewi Rahmawati Laura Mahendratta Tjahjono Lestari, Caecilia Citra Mohammad Fatoni Anggris, Mohammad Fatoni Mychael Maoeretz Engel Naya, Rafi Abhista Nicholas Dylan Lienardi Noviando Yuliando Owen Orlando Patrick Steven Kent Sugiarto Rarasmaya Indraswari Reinaldo Lewis Lordianto Reiner Anggriawan Jasin Rinabi Tanamal Rudy Susanto Rudy Susanto Safri Adam Setiawan, Averill Saladin Atma Siswanto, Felicia Devina Sutanto, Richard Evan Theresia Ratih Dewi Saputri Timotius Timotius Leonardo Lianoto Tota Pirdo Kasih Tota Pirdo Kasih Yaurentius, Evelyn Callista Yobel Nathaniel Filipus Yuwono Marta Dinata